library(tidyverse)
library(tidyr)
library(dplyr)
library(readr)
library(ggplot2)


steam_data <- read.csv(url("https://raw.githubusercontent.com/CaptainBlast096/Intro-to-Data-Science---Final-Project/main/data/steam.csv?token=AURDUTODKXAEQX3HN6XF2DDBCFCQI"))

steam_data
Warning in for (i in seq_along(cenv$extra)) { :
  closing unused connection 3 (https://raw.githubusercontent.com/CaptainBlast096/Intro-to-Data-Science---Final-Project/main/data/steam.csv?token=AURDUTNV5Q6XH2E6BMHB5ADBCCASW)
colnames(steam_data)
 [1] "appid"            "name"             "release_date"     "english"          "developer"        "publisher"        "platforms"       
 [8] "required_age"     "categories"       "genres"           "steamspy_tags"    "achievements"     "positive_ratings" "negative_ratings"
[15] "average_playtime" "median_playtime"  "owners"           "price"           
steam_filtered <- steam_data %>% 
  filter (genres %in% c("Action", "FPS", "Sci-Fi", "Puzzle", "Indie","Strategy", "Racing","RPG", "Casual", "Horror", "Stealth", "Shooter", "Simulation"))
steam_filtered #Don't use

#Lines filter the ammount of row in each genre

Action <- steam_data %>%  filter(str_detect(steamspy_tags, "Action"))
Action
nrow(Action)
[1] 10344
FPS <- steam_data %>%  filter(str_detect(steamspy_tags, "FPS"))
FPS
nrow(FPS)
[1] 405
Shooter <- steam_data %>%  filter(str_detect(steamspy_tags, "Shooter"))
Shooter
nrow(Shooter)
[1] 227
Sci_fi <- steam_data %>%  filter(str_detect(steamspy_tags, "Sci-fi"))
Sci_fi
nrow(Sci_fi) 
[1] 157
Puzzle <- steam_data %>%  filter(str_detect(steamspy_tags, "Puzzle"))
Puzzle
nrow(Puzzle)
[1] 1167
Indie <- steam_data %>%  filter(str_detect(steamspy_tags, "Indie"))
Indie
nrow(Indie)
[1] 16232
Strategy <- steam_data %>%  filter(str_detect(steamspy_tags, "Strategy"))
Strategy
nrow(Strategy)
[1] 4180
Racing <- steam_data %>%  filter(str_detect(steamspy_tags, "Racing"))
Racing
nrow(Racing)
[1] 765
RPG <- steam_data %>%  filter(str_detect(steamspy_tags, "RPG"))
RPG
nrow(RPG)
[1] 2863
Casual <- steam_data %>%  filter(str_detect(steamspy_tags, "Casual"))
Casual
nrow(Casual)
[1] 8205
Horror <- steam_data %>%  filter(str_detect(steamspy_tags, "Horror"))
Horror
nrow(Horror)
[1] 566
Simulation <- steam_data %>%  filter(str_detect(steamspy_tags, "Simulation"))
Simulation
nrow(Simulation)
[1] 3284
 #Genres

Top_Games <- data.frame (Genres = c("Action", "FPS", "Shooter", "Sci-fi", "Puzzle", "Indie", "Strategy", "Racing", "RPG", "Casual", "Horror", "Simulation"), Amount = c(10344,405,227,157,1167,16232,4180,765,2863,8205,566,3284))


#ggplot(Top_Games, aes(x = Genres, y = Ammount_of_games)) + geom_col(fill = "black") 
ggplot(Top_Games, aes(x = Genres, y = Amount, fill = Genres,)) + geom_bar(stat = "identity", width = 0.5) + coord_flip()

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